A Random Forest Model Based on Lidar and Field Measurements for Parameterizing Surface Roughness in Coastal Modeling

Abstract

A novel technique for parameterizing surface roughness in coastal inundation models using airborne laser scanning (lidar) data is presented. Two important parameters to coastal overland flow dynamics, Manning’s ${\bm {n}}$ (bottom friction) and effective aerodynamic roughness length (wind speed reduction), are computed based on a random forest (RM) regression model trained using field measurements from 24 sites in Florida fused with georegistered lidar point cloud data. The lidar point cloud for each test site is separated into ground and nonground classes and the z-dimensional (height or elevation) variance from the least squares regression plane is computed, along with the height of the nonground regression plane. These statistics serve as the predictor variables in the parameterization model. The model is then tested using a bootstrap subsampling procedure consisting of removal without replacement of one record and using the surviving records to train the model and predict the surface roughness parameter of the removed record. When compared with the industry standard technique of assigning surface roughness parameters based on published land use/land cover type, the RM regression models reduce the parameterization error by 93% (0.086–0.006) and 53% (1.299–0.610 m) for Manning’s ${\bm {n}}$ and effective aerodynamic roughness length, respectively. These improvements will improve water level and velocity predictions in coastal models.